{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a116da67-5248-4227-b4b3-15caecdb5e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "##库的导入\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from scipy.stats import zscore\n",
    "import scipy.cluster.hierarchy as sch\n",
    "import pylab as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from factor_analyzer import FactorAnalyzer as FA\n",
    "from factor_analyzer.factor_analyzer import calculate_kmo\n",
    "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "import libpysal\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import transbigdata as tbd\n",
    "import esda\n",
    "from pyproj import CRS\n",
    "from splot.esda import moran_scatterplot\n",
    "from sklearn.metrics import davies_bouldin_score\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5f28b14-8be5-4628-a1ad-b9c9f9a74706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province_name</th>\n",
       "      <th>0</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.585112</td>\n",
       "      <td>116.403039</td>\n",
       "      <td>39.914909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.439347</td>\n",
       "      <td>117.216419</td>\n",
       "      <td>39.143831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.252453</td>\n",
       "      <td>114.470479</td>\n",
       "      <td>38.074112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.143309</td>\n",
       "      <td>112.594097</td>\n",
       "      <td>37.867070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.185024</td>\n",
       "      <td>111.671565</td>\n",
       "      <td>40.837895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.254935</td>\n",
       "      <td>123.401674</td>\n",
       "      <td>41.800522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.217271</td>\n",
       "      <td>125.330606</td>\n",
       "      <td>43.918866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.181648</td>\n",
       "      <td>126.637622</td>\n",
       "      <td>45.766187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.723532</td>\n",
       "      <td>121.480324</td>\n",
       "      <td>31.236546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.408812</td>\n",
       "      <td>118.804048</td>\n",
       "      <td>32.095658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.628952</td>\n",
       "      <td>120.189720</td>\n",
       "      <td>30.248403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.395149</td>\n",
       "      <td>117.324215</td>\n",
       "      <td>31.889927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.526387</td>\n",
       "      <td>119.326018</td>\n",
       "      <td>26.121393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.347137</td>\n",
       "      <td>115.924376</td>\n",
       "      <td>28.669681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.370880</td>\n",
       "      <td>117.126952</td>\n",
       "      <td>36.662279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.310148</td>\n",
       "      <td>113.666819</td>\n",
       "      <td>34.751785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.322763</td>\n",
       "      <td>114.308774</td>\n",
       "      <td>30.548863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.363971</td>\n",
       "      <td>112.945470</td>\n",
       "      <td>28.235017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.731980</td>\n",
       "      <td>113.263227</td>\n",
       "      <td>23.154431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.297873</td>\n",
       "      <td>108.321874</td>\n",
       "      <td>22.832993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.490977</td>\n",
       "      <td>110.168690</td>\n",
       "      <td>20.034768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.332140</td>\n",
       "      <td>106.554988</td>\n",
       "      <td>29.555927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.329895</td>\n",
       "      <td>104.064264</td>\n",
       "      <td>30.663940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>106.709874</td>\n",
       "      <td>26.563191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.276342</td>\n",
       "      <td>102.728219</td>\n",
       "      <td>25.022114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.327902</td>\n",
       "      <td>91.179248</td>\n",
       "      <td>29.660176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.123212</td>\n",
       "      <td>108.970163</td>\n",
       "      <td>34.284398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.150609</td>\n",
       "      <td>103.833182</td>\n",
       "      <td>36.066455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.120691</td>\n",
       "      <td>106.179082</td>\n",
       "      <td>38.498117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.223041</td>\n",
       "      <td>106.179082</td>\n",
       "      <td>38.498117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    province_name         0         lon        lat\n",
       "0               0  0.585112  116.403039  39.914909\n",
       "1               1  0.439347  117.216419  39.143831\n",
       "2               2  0.252453  114.470479  38.074112\n",
       "3               3  0.143309  112.594097  37.867070\n",
       "4               4  0.185024  111.671565  40.837895\n",
       "5               5  0.254935  123.401674  41.800522\n",
       "6               6  0.217271  125.330606  43.918866\n",
       "7               7  0.181648  126.637622  45.766187\n",
       "8               8  0.723532  121.480324  31.236546\n",
       "9               9  0.408812  118.804048  32.095658\n",
       "10             10  0.628952  120.189720  30.248403\n",
       "11             11  0.395149  117.324215  31.889927\n",
       "12             12  0.526387  119.326018  26.121393\n",
       "13             13  0.347137  115.924376  28.669681\n",
       "14             14  0.370880  117.126952  36.662279\n",
       "15             15  0.310148  113.666819  34.751785\n",
       "16             16  0.322763  114.308774  30.548863\n",
       "17             17  0.363971  112.945470  28.235017\n",
       "18             18  0.731980  113.263227  23.154431\n",
       "19             19  0.297873  108.321874  22.832993\n",
       "20             20  0.490977  110.168690  20.034768\n",
       "21             21  0.332140  106.554988  29.555927\n",
       "22             22  0.329895  104.064264  30.663940\n",
       "23             23  0.253892  106.709874  26.563191\n",
       "24             24  0.276342  102.728219  25.022114\n",
       "25             25  0.327902   91.179248  29.660176\n",
       "26             26  0.123212  108.970163  34.284398\n",
       "27             27  0.150609  103.833182  36.066455\n",
       "28             28  0.120691  106.179082  38.498117\n",
       "29             29  0.223041  106.179082  38.498117"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#地理数据读取\n",
    "gdp_data = pd.read_csv('pingjia5.csv',encoding='ascii')  #导入数据\n",
    "geography_data = pd.read_csv('province_geography_data.csv',encoding='GB2312')\n",
    "gdp_data2 = pd.read_csv('pingjia0.csv',encoding='ascii')\n",
    "data_stop = pd.merge(gdp_data, geography_data, on='province_name')\n",
    "data_stop2 = pd.merge(gdp_data2, geography_data, on='province_name')\n",
    "data_stop.head(30)\n",
    "data_stop2.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bd93a5a-7976-48dd-a948-b91f1ef1844a",
   "metadata": {},
   "outputs": [],
   "source": [
    "###指标的处理\n",
    "file_path='D:/shuju/shuju0.xlsx'\n",
    "df66=pd.read_excel(file_path,sheet_name=5)\n",
    "df11=pd.read_excel(file_path,sheet_name=0)\n",
    "df22=pd.read_excel(file_path,sheet_name=1)\n",
    "df33=pd.read_excel(file_path,sheet_name=2)\n",
    "df44=pd.read_excel(file_path,sheet_name=3)\n",
    "df55=pd.read_excel(file_path,sheet_name=4)\n",
    "def defen(df):\n",
    "    df=df.drop(['省/市地区','年份','乡村人口（万人）','耕地面积(公顷)','人均耕地面积（公顷）'],axis=1)\n",
    "    # 按列取最大值、最小值,inplace=True)\n",
    "    max = df.max(axis=0)\n",
    "    min = df.min(axis=0)\n",
    "    # 先全按照正向指标处理\n",
    "    b = (df - min) / (max - min)\n",
    "    # print(max.iloc[2])\n",
    "    # 处理反向指标\n",
    "    for i in [0, 2, 3, 5, 6, 7, 8, 9]:\n",
    "        b.iloc[:, i] = (max.iloc[i] - df.iloc[:, i]) / (max.iloc[i] - min.iloc[i])\n",
    "    # print(b)\n",
    "    data = b + 0.01\n",
    "    ###\n",
    "    ####计算五个得分\n",
    "    # 求出评价对象数目\n",
    "    n = data.shape[0]\n",
    "    # 求和\n",
    "    s = data.sum(axis=0)\n",
    "    # 求比重矩阵\n",
    "    P = data / s\n",
    "    # 求熵以及差异指数\n",
    "    e = -(P * np.log(P)).sum(axis=0) / np.log(n)\n",
    "    g = 1 - e\n",
    "    # 求出权重\n",
    "    w = g / sum(g)\n",
    "    f = P @ w\n",
    "    # print(P)\n",
    "    # print(w)\n",
    "    ind1 = np.argsort(-f)\n",
    "    # print(ind1)\n",
    "    ind11 = np.zeros(31)\n",
    "    ind11[ind1] = np.arange(1, 32)\n",
    "    # print(ind11)#输出排序\n",
    "    indexx = ['产业兴旺', '生态文明', '乡风文明', '治理有效', '生活富裕']\n",
    "    scound = P * w  # 第二指标得分\n",
    "    chanye = scound.iloc[:, 0] + scound.iloc[:, 1]\n",
    "    shengtai = scound.iloc[:, 2] + scound.iloc[:, 3]\n",
    "    xiangfeng = scound.iloc[:, 6] + scound.iloc[:, 5]\n",
    "    zhili = scound.iloc[:, 9] + scound.iloc[:, 7] + scound.iloc[:, 4]\n",
    "    shenghuo = scound.iloc[:, 10] + scound.iloc[:, 12] + scound.iloc[:, 8] + scound.iloc[:, 11]\n",
    "    combined = np.vstack((chanye, shengtai, xiangfeng, zhili, shenghuo))\n",
    "    ai = pd.DataFrame(combined.T)\n",
    "    d = ai * 1000\n",
    "    #print(d)\n",
    "    # 计算KMO与p值验证因子分析合理性\n",
    "    kmo_all, kmo_model = calculate_kmo(d)\n",
    "\n",
    "    ##bartlett球状检验\n",
    "    chi_square_value, p_value = calculate_bartlett_sphericity(d)\n",
    "\n",
    "    # 输出检验结果\n",
    "    # print('卡方值:', chi_square_value)\n",
    "    # print('p值:', p_value)\n",
    "\n",
    "    r = np.corrcoef(ai.T)  # 相关系数矩阵（原指标）\n",
    "    print(r)\n",
    "    val, vec = np.linalg.eig(r)  # 求特征值\n",
    "    cs = np.cumsum(val)  # 求特征值的累加值\n",
    "    rate = val / cs[-1]\n",
    "    srate = np.cumsum(rate)\n",
    "    # print(srate)\n",
    "    n = 3  # 主因子个数\n",
    "\n",
    "    fa = FA(3, rotation='varimax')\n",
    "    fa.fit(d)  # 求解方差最大的模型\n",
    "    A = fa.loadings_  # 求解载荷矩阵\n",
    "    gx = np.sum(A ** 2, axis=0)  # 计算方差贡献\n",
    "    s2 = 1 - np.sum(A ** 2, axis=1)  ###计算特殊方差1\n",
    "    ss = np.linalg.inv(np.diag(s2))\n",
    "    f = ss @ A @ np.linalg.inv(A.T @ ss @ A)  # 计算因子得分函数的系数\n",
    "    df = d @ f  # 计算因子得分\n",
    "    pingjia = df @ gx / sum(gx)\n",
    "    return pingjia,df,p_value,kmo_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "feceea6f-6a28-4304-9d04-e437ec0375ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -0.35978715 -0.08910767  0.11730785  0.14308166]\n",
      " [-0.35978715  1.         -0.33065688 -0.40698552 -0.49099835]\n",
      " [-0.08910767 -0.33065688  1.          0.26409685  0.09434725]\n",
      " [ 0.11730785 -0.40698552  0.26409685  1.          0.55435497]\n",
      " [ 0.14308166 -0.49099835  0.09434725  0.55435497  1.        ]]\n",
      "[[ 1.         -0.43092146 -0.19023063  0.25876806  0.39723597]\n",
      " [-0.43092146  1.         -0.28321021 -0.48879484 -0.43319467]\n",
      " [-0.19023063 -0.28321021  1.          0.31197197 -0.29208092]\n",
      " [ 0.25876806 -0.48879484  0.31197197  1.          0.41968178]\n",
      " [ 0.39723597 -0.43319467 -0.29208092  0.41968178  1.        ]]\n",
      "[[ 1.         -0.49972494 -0.02995401  0.1894773   0.33254117]\n",
      " [-0.49972494  1.         -0.15400022 -0.29043426 -0.46541565]\n",
      " [-0.02995401 -0.15400022  1.          0.36405216 -0.20828389]\n",
      " [ 0.1894773  -0.29043426  0.36405216  1.          0.52473798]\n",
      " [ 0.33254117 -0.46541565 -0.20828389  0.52473798  1.        ]]\n",
      "[[ 1.         -0.64179241 -0.00668783  0.30057883  0.31039092]\n",
      " [-0.64179241  1.         -0.11954239 -0.36986432 -0.58206014]\n",
      " [-0.00668783 -0.11954239  1.          0.31432314 -0.13729125]\n",
      " [ 0.30057883 -0.36986432  0.31432314  1.          0.47280189]\n",
      " [ 0.31039092 -0.58206014 -0.13729125  0.47280189  1.        ]]\n",
      "[[ 1.         -0.49750946  0.06101368 -0.02252569 -0.09030425]\n",
      " [-0.49750946  1.         -0.21478017 -0.18184946 -0.49992426]\n",
      " [ 0.06101368 -0.21478017  1.          0.27948215 -0.01036663]\n",
      " [-0.02252569 -0.18184946  0.27948215  1.          0.43148202]\n",
      " [-0.09030425 -0.49992426 -0.01036663  0.43148202  1.        ]]\n",
      "[[ 1.         -0.64016347  0.09598384  0.37079448  0.29487221]\n",
      " [-0.64016347  1.         -0.21019812 -0.52388953 -0.5406282 ]\n",
      " [ 0.09598384 -0.21019812  1.          0.14125792 -0.25333991]\n",
      " [ 0.37079448 -0.52388953  0.14125792  1.          0.56640149]\n",
      " [ 0.29487221 -0.5406282  -0.25333991  0.56640149  1.        ]]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster assignments: [3 3 1 2 2 2 2 3 4 3 4 3 3 3 1 1 3 1 4 3 4 3 3 2 3 3 2 2 2 2 2]\n",
      "[ 0.51088151  0.46552144  0.64788029  0.59693318  0.51651949  0.41690163\n",
      "  0.24986269  0.21771285  0.37097422  0.52476368  0.0918947   0.5573653\n",
      "  0.33978153  0.34028912  0.75301424  0.71790045  0.17083565  0.70188769\n",
      "  0.27424439  0.44967896  0.45533852  0.43799564  0.48752672  0.53968795\n",
      "  0.36922667  0.16260956 -0.20805557  0.61545389  0.30539336  0.53970629\n",
      "  0.47626619]\n"
     ]
    }
   ],
   "source": [
    "provinces=df66.iloc[:,0]\n",
    "provinces=pd.DataFrame(provinces)\n",
    "#计算各年份得分\n",
    "pingjia0,yinzi0,p0,kmo0=defen(df11)\n",
    "pingjia1,yinzi1,p1,kmo1=defen(df22)\n",
    "pingjia2,yinzi2,p2,kmo2=defen(df33)\n",
    "pingjia3,yinzi3,p3,kmo3=defen(df44)\n",
    "pingjia4,yinzi4,p4,kmo4=defen(df55)\n",
    "pingjia5,yinzi5,p5,kmo5=defen(df66)\n",
    "data_arrays = [pingjia0, pingjia1, pingjia2, pingjia3, pingjia4, pingjia5]\n",
    "\n",
    "# 合并数组\n",
    "combined_data = np.column_stack(data_arrays)\n",
    "\n",
    "# 计算平均值\n",
    "mean_values = np.mean(combined_data, axis=1)\n",
    "\n",
    "# 将平均值作为新的一列添加到合并后的数据中\n",
    "combined_data=pd.DataFrame(combined_data)\n",
    "combined_data['综合得分'] = mean_values\n",
    "combined_data_sorted = combined_data.sort_values(by='综合得分', ascending=False)\n",
    "combined_data_sorted['排名'] = combined_data_sorted['综合得分'].rank(method='first', ascending=False)\n",
    "# 将numpy数组转换为pandas DataFrame，并添加列名\n",
    "df_ = pd.DataFrame(combined_data)\n",
    "df_.columns = ['2015年', '2016年', '2017年', '2019年', '2020年', '2021年', '综合得分']\n",
    "\n",
    "# 基于平均值进行排序，并获取排名（注意：这里假设你想要降序排名）\n",
    "provinces=df66.iloc[:,0]\n",
    "provinces=pd.DataFrame(provinces)\n",
    "# 将排名作为新的一列添加到DataFrame中\n",
    "combined_data_sorted = pd.concat([provinces, combined_data_sorted], axis=1)\n",
    "df_=combined_data_sorted\n",
    "df_['排名'] = df_['排名'].astype(int)\n",
    "df_.columns=['省/市地区','2015年','2016年','2017年','2019年','2020年','2021年','综合得分','排名']\n",
    "# 打印结果\n",
    "#print(df_)\n",
    "df_.to_excel('paiming.xlsx')\n",
    "pingjia5.to_excel('pingjia5.xlsx')\n",
    "pingjia0.to_excel('pingjia0.xlsx')\n",
    "###综合排名柱状图\n",
    "df_sorted =df_.sort_values(by='综合得分', ascending=False)\n",
    "#print(df_sorted)\n",
    "plt.barh(df_sorted['省/市地区'], df_sorted['综合得分'],color='r')\n",
    "# 设置标题和坐标轴标签\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.title('按序号排序的省份/市地区得分柱状图')\n",
    "plt.xlabel('省/市地区')\n",
    "plt.ylabel('综合得分')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram, fcluster\n",
    "from scipy.spatial.distance import pdist\n",
    "from sklearn import metrics\n",
    "distance_matrix = pdist(yinzi5, 'euclidean')\n",
    "Z = linkage(distance_matrix, method='ward')\n",
    "plt.figure(figsize=(10, 7))\n",
    "dendrogram(Z)\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Sample index')\n",
    "plt.ylabel('Distance')\n",
    "plt.show()\n",
    "num_clusters = 4\n",
    "labels = fcluster(Z, num_clusters, criterion='maxclust')\n",
    "print(\"Cluster assignments:\", labels)\n",
    "silhouette_samples = metrics.silhouette_samples(yinzi5, labels)\n",
    "print(silhouette_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8902a058-09cc-48b5-bb2c-034722b784d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0         1         2\n",
      "category                               \n",
      "1         10.188679  1.864925  2.240638\n",
      "2         10.099710 -4.620771  2.772625\n",
      "3         16.411333 -4.020392  2.900301\n",
      "4         25.213226 -5.987950  3.428566\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制样本的轮廓系数直方图\n",
    "plt.hist(silhouette_samples, bins=50, range=[-0.1, 1])\n",
    "plt.xlabel('Silhouette Coefficient Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Silhouette Coefficient Histogram')\n",
    "plt.show()\n",
    "#计算每一类每项因子均值\n",
    "N=yinzi5.shape[0]\n",
    "yinzi5['category'] = labels\n",
    "mean_values = yinzi5.groupby('category').mean()\n",
    "print(mean_values)\n",
    "mean_values.columns=['生活生态和谐因子','治理有效因子','乡风文明因子']\n",
    "mean_values.to_excel('julei.xlsx')\n",
    "\n",
    "# 分析明显有短板乡村\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 绘制散点图\n",
    "ax.scatter(yinzi5.iloc[:,0], yinzi5.iloc[:,1], yinzi5.iloc[:,2])\n",
    "for i, txt in enumerate(yinzi5.index):\n",
    "    x, y, z = yinzi5.iloc[i, 0], yinzi5.iloc[i, 1], yinzi5.iloc[i, 2]\n",
    "    ax.text(x, y, z, txt, fontsize=5)  # 调整fontsize以适合您的图形\n",
    "# 设置标签\n",
    "ax.set_xlabel('生活生态和谐因子')\n",
    "ax.set_ylabel('治理有效因子')\n",
    "ax.set_zlabel('乡风文明因子')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ebb8ca1-7d7f-461b-9dd7-b42c439948a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      省份/市地区        得分\n",
      "18       广东省  0.665893\n",
      "20       海南省  0.648331\n",
      "8        上海市  0.625643\n",
      "10       浙江省  0.617401\n",
      "12       福建省  0.541453\n",
      "9        江苏省  0.523860\n",
      "1        天津市  0.510402\n",
      "21       重庆市  0.475769\n",
      "11       安徽省  0.468394\n",
      "0        北京市  0.438721\n",
      "17       湖南省  0.432318\n",
      "14       山东省  0.421086\n",
      "19   广西壮族自治区  0.420499\n",
      "22       四川省  0.413964\n",
      "24       云南省  0.401937\n",
      "16       湖北省  0.397955\n",
      "13       江西省  0.394976\n",
      "25     西藏自治区  0.390692\n",
      "15       河南省  0.377916\n",
      "2        河北省  0.364085\n",
      "7       黑龙江省  0.354571\n",
      "26       陕西省  0.303147\n",
      "6        吉林省  0.291613\n",
      "23       贵州省  0.288054\n",
      "28       青海省  0.277305\n",
      "5        辽宁省  0.275471\n",
      "29   宁夏回族自治区  0.268321\n",
      "30  新疆维吾尔自治区  0.249416\n",
      "3        山西省  0.240608\n",
      "4     内蒙古自治区  0.236755\n",
      "27       甘肃省  0.229553\n"
     ]
    }
   ],
   "source": [
    "#提取省份列\n",
    "provinces=df66.iloc[:,0]\n",
    "provinces=pd.DataFrame(provinces)\n",
    "####乡村发展潜力\n",
    "cp=yinzi5.max(axis=0)\n",
    "cm=yinzi5.min(axis=0)\n",
    "p1=np.linalg.norm(yinzi5-cp,axis=1)\n",
    "p2=np.linalg.norm(yinzi5-cm,axis=1)\n",
    "ff=p2/(p1+p2)\n",
    "ff=pd.DataFrame(ff)\n",
    "ff.insert(0, '省份/市地区', provinces)\n",
    "ff.columns=['省份/市地区','得分']\n",
    "ff_sorted = ff.sort_values(by='得分', ascending=False)\n",
    "print(ff_sorted)\n",
    "ff_sorted.to_excel('qianli.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8eab91cb-c0da-4cd1-b929-cd5066dd1f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#空间\n",
    "lat1sh=20.0347675\n",
    "lat2sh=45.766287\n",
    "lon1sh=87.5354511\n",
    "lon2sh=126.6376322\n",
    "bounds=[lon1sh,lat1sh,lon2sh,lat2sh] #设置地理区域的边界（经纬度）\n",
    "params=tbd.area_to_params(bounds,accuracy=100)   #将地理区域边界转换为网格参数\n",
    "#print(params)\n",
    "data_stop['LONCOL'],data_stop['LATCOL']=tbd.GPS_to_grid(data_stop['lon'],data_stop['lat'],params)\n",
    "data_count=data_stop.groupby(['LONCOL','LATCOL'])\n",
    "#['lon'].count().rename('count').reset_index()  #按网格列和行索引对data_stop进行分组，并计算每个网格中的lon数量（可能是点的数量），然后重命名并重置索引。\n",
    "data_count=data_stop.groupby(['LONCOL','LATCOL']).size().rename('count').reset_index()\n",
    "data_count['geometry']=tbd.grid_to_polygon([data_count['LONCOL'],data_count['LATCOL']],params)  #将网格坐标转换\n",
    "data_count['0']=data_stop['0']  #将data_stop中的'0'列复制到data_count中\n",
    "data_count['province_name']=data_stop['province_name']\n",
    "data_count=gpd.GeoDataFrame(data_count)  #将data_count转换为GeoDataFrame\n",
    "data_count.head(40)\n",
    "\n",
    "data_count.crs=CRS('EPSG:4326')\n",
    "data_count_2416=data_count.to_crs(epsg='2416')  #将data_count的坐标参考系统转换为EPSG:2416\n",
    "data=data_count_2416.copy()  #复制到data\n",
    "k=10\n",
    "wq=libpysal.weights.KNN.from_dataframe(data,k=k)  #使用libpysal库创建一个基于K最近邻（KNN）的空间权重对象，其中K设为10\n",
    "#full_weights_matrix = wq.full()\n",
    "#print(full_weights_matrix)\n",
    "#print(wq)\n",
    "#wq=libpysal.weights.KNN.from_dataframe(data,k=10)\n",
    "#print(wq)\n",
    "weight=data['0']  #将data中的'0'列赋值给weight变量。\n",
    "lisa_moran=esda.moran.Moran(weight,wq)  #计算全局Moran's I统计量\n",
    "lisa_moran.I,lisa_moran.z_norm,lisa_moran.p_sim  #获取全局Moran's I统计量的值、标准化Z值和模拟的p值\n",
    "#print(lisa_moran.I)\n",
    "lisa_moran_local=esda.moran.Moran_Local(weight,wq)  #计算局部莫兰指数\n",
    "data['Is']=lisa_moran_local.Is\n",
    "data['z']=lisa_moran_local.z_sim  #得到Is和z\n",
    "data.insert(0, '省份/市地区', provinces)\n",
    "data.to_excel('moran.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98f9a1bb-5f71-456a-880b-81027f5b29f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "plt.figure(figsize=(10, 6))  #创建一个新的图形窗口，并设置其大小为10x6英寸\n",
    "plt.scatter(data['Is'], data['z'], c=data['Is'], cmap='coolwarm', vmin=-1, vmax=1)  #绘制一个散点图\n",
    "coeffs, cov_matrix = np.polyfit(data['Is'], data['z'], 1, cov=True)  #使用numpy的polyfit函数对data中的'Is'和'z'列进行线性拟合，并返回拟合系数和协方差矩阵。这里拟合的是一条直线\n",
    "slope = coeffs[0]\n",
    "intercept = coeffs[1] #从拟合系数中提取直线的斜率和截距\n",
    "fit_x = np.linspace(min(data['Is']), max(data['Is']), 100)  #创建一个包含100个点的线性空间数组，这些点均匀地分布在data中'Is'列的最小值和最大值之间\n",
    "fit_y = slope * fit_x + intercept  #使用前面得到的斜率和截距来计算线性拟合线上每个点的y值\n",
    "\n",
    "plt.plot(fit_x, fit_y, 'black', label='Linear Fit: y = %.2fx + %.2f' % (slope, intercept)) #在散点图上绘制线性拟合线，颜色为黑色，并添加图例标签，显示线性拟合的方程\n",
    "plt.colorbar(label='Is')  #显示颜色条\n",
    "plt.title('Local Moran')\n",
    "plt.xlabel('Local_Moran')\n",
    "plt.ylabel('z')\n",
    "for i, txt in enumerate(data_count['province_name']):\n",
    "    plt.annotate(txt, (data['Is'][i], data['z'][i]))\n",
    "plt.show()\n",
    "plt.savefig(\"1.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "695b2e4e-5794-4efd-a4db-583907f58e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "###15年\n",
    "lat1sh=20.0347675\n",
    "lat2sh=45.766287\n",
    "lon1sh=87.5354511\n",
    "lon2sh=126.6376322\n",
    "bounds=[lon1sh,lat1sh,lon2sh,lat2sh] #设置地理区域的边界（经纬度）\n",
    "params=tbd.area_to_params(bounds,accuracy=100)   #将地理区域边界转换为网格参数\n",
    "#print(params)\n",
    "data_stop2['LONCOL'],data_stop2['LATCOL']=tbd.GPS_to_grid(data_stop2['lon'],data_stop2['lat'],params)\n",
    "data_count=data_stop2.groupby(['LONCOL','LATCOL'])\n",
    "#['lon'].count().rename('count').reset_index()  #按网格列和行索引对data_stop进行分组，并计算每个网格中的lon数量（可能是点的数量），然后重命名并重置索引。\n",
    "data_count=data_stop2.groupby(['LONCOL','LATCOL']).size().rename('count').reset_index()\n",
    "data_count['geometry']=tbd.grid_to_polygon([data_count['LONCOL'],data_count['LATCOL']],params)  #将网格坐标转换\n",
    "data_count['0']=data_stop2['0']  #将data_stop中的'0'列复制到data_count中\n",
    "data_count['province_name']=data_stop2['province_name']\n",
    "data_count=gpd.GeoDataFrame(data_count)  #将data_count转换为GeoDataFrame\n",
    "data_count.head(40)\n",
    "\n",
    "data_count.crs=CRS('EPSG:4326')\n",
    "data_count_2416=data_count.to_crs(epsg='2416')  #将data_count的坐标参考系统转换为EPSG:2416\n",
    "data=data_count_2416.copy()  #复制到data\n",
    "k=10\n",
    "wq=libpysal.weights.KNN.from_dataframe(data,k=k)  #使用libpysal库创建一个基于K最近邻（KNN）的空间权重对象，其中K设为10\n",
    "#full_weights_matrix = wq.full()\n",
    "#print(full_weights_matrix)\n",
    "#print(wq)\n",
    "#wq=libpysal.weights.KNN.from_dataframe(data,k=10)\n",
    "#print(wq)\n",
    "weight=data['0']  #将data中的'0'列赋值给weight变量。\n",
    "lisa_moran=esda.moran.Moran(weight,wq)  #计算全局Moran's I统计量\n",
    "lisa_moran.I,lisa_moran.z_norm,lisa_moran.p_sim  #获取全局Moran's I统计量的值、标准化Z值和模拟的p值\n",
    "#print(lisa_moran.I)\n",
    "lisa_moran_local=esda.moran.Moran_Local(weight,wq)  #计算局部莫兰指数\n",
    "data['Is']=lisa_moran_local.Is\n",
    "data['z']=lisa_moran_local.z_sim  #得到Is和z\n",
    "data.insert(0, '省份/市地区', provinces)\n",
    "data.to_excel('moran15.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a3fe158-f592-4058-8b49-4b52dd81c0b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "plt.figure(figsize=(10, 6))  #创建一个新的图形窗口，并设置其大小为10x6英寸\n",
    "plt.scatter(data['Is'], data['z'], c=data['Is'], cmap='coolwarm', vmin=-1, vmax=1)  #绘制一个散点图\n",
    "coeffs, cov_matrix = np.polyfit(data['Is'], data['z'], 1, cov=True)  #使用numpy的polyfit函数对data中的'Is'和'z'列进行线性拟合，并返回拟合系数和协方差矩阵。这里拟合的是一条直线\n",
    "slope = coeffs[0]\n",
    "intercept = coeffs[1] #从拟合系数中提取直线的斜率和截距\n",
    "fit_x = np.linspace(min(data['Is']), max(data['Is']), 100)  #创建一个包含100个点的线性空间数组，这些点均匀地分布在data中'Is'列的最小值和最大值之间\n",
    "fit_y = slope * fit_x + intercept  #使用前面得到的斜率和截距来计算线性拟合线上每个点的y值\n",
    "\n",
    "plt.plot(fit_x, fit_y, 'black', label='Linear Fit: y = %.2fx + %.2f' % (slope, intercept)) #在散点图上绘制线性拟合线，颜色为黑色，并添加图例标签，显示线性拟合的方程\n",
    "plt.colorbar(label='Is')  #显示颜色条\n",
    "plt.title('Local Moran')\n",
    "plt.xlabel('Local_Moran')\n",
    "plt.ylabel('z')\n",
    "for i, txt in enumerate(data_count['province_name']):\n",
    "    plt.annotate(txt, (data['Is'][i], data['z'][i]))\n",
    "plt.show()\n",
    "plt.savefig(\"2.jpg\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669de36f-cf8b-4133-a568-2c23b6b4a673",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
